The scikit-learn library offers the LinearRegression class, which works with n-dimensional spaces. For this purpose, we're going to use the Boston dataset:
from sklearn.datasets import load_boston
boston = load_boston()
print(boston.data.shape)
(506L, 13L)
print(boston.target.shape)
(506L,)
It has 506 samples with 13 input features and one output. In the following graph, there's a collection of the plots of the first 12 features:
The plot of the first 12 features of the Boston dataset
When working with datasets, it's useful to have a tabular view to manipulate data. Pandas is a perfect framework for this task, and even though it's beyond the scope of this book, I suggest you create a data frame with the pandas.DataFrame(boston.data, columns=boston.feature_names) command and use Jupyter to visualize it...
